# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Basic word2vec example."""

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import collections
import math
import os
import random
from tempfile import gettempdir
import zipfile

import numpy as np
from six.moves import urllib
from six.moves import xrange  # pylint: disable=redefined-builtin
import tensorflow as tf
import json

# Step 1: 读取文件
"""
def read_data(filename):
    #   读取中文宋词
    line_words = []

    with open(filename, "r", encoding="UTF-8") as f:
        line = f.readline()
        while line:
            while '\n' in line:
                line = line.replace('\n', '')
            line_words.append(line)
            line = f.readline()
    # 将所有字符(汉字+标点符号)拆开
    words = []
    for lw in line_words:
        words += [w for w in lw]

    return words
"""

def read_data(filename):
    with open(filename, encoding="utf-8") as f:
        data = f.read()
    data = list(data)
    return data


filename = 'QuanSongCi.txt'
vocabulary = read_data(filename)
print('Data size', len(vocabulary))

# Step 2: Build the dictionary and replace rare words with ? token.
# 取出现频率最高的词的数量组成字典(前vocabulary_size高频率字符)，不在字典中的字用'NUK'代替

vocabulary_size = 5000


def build_dataset(words, n_words):
    """Process raw inputs into a dataset."""
    count = [['NUK', -1]]
    count.extend(collections.Counter(words).most_common(n_words - 1))
    dictionary = dict()

    for word, _ in count:
        dictionary[word] = len(dictionary)

    data = list()
    unk_count = 0

    for word in words:
        index = dictionary.get(word, 0)

        if index == 0:  # dictionary['NUk']
            unk_count += 1

        data.append(index)

    count[0][1] = unk_count
    reversed_dictionary = dict(zip(dictionary.values(), dictionary.keys()))

    return data, count, dictionary, reversed_dictionary


# Filling 4 global variables:
# data - list of codes (integers from 0 to vocabulary_size-1).
#   This is the original text but words are replaced by their codes
# count - map of words(strings) to count of occurrences
# dictionary - map of words(strings) to their codes(integers)
# reverse_dictionary - maps codes(integers) to words(strings)
data, count, dictionary, reversed_dictionary = build_dataset(vocabulary, vocabulary_size)

del vocabulary  # Hint to reduce memory.
print('Most common words (+NUK)', count[:5])
print('Sample data', data[:10], [reversed_dictionary[i] for i in data[:10]])
"""
count是字典，分别是word,word出现的次数(词频)，顺序由出现最多的往最少的排列
dictionary:是字典，分别是word,序号(也就是在count中的标号)
data对应的是列表:元素是所有的数据对应的索引，也就是将word转换成了整数索引
reversed_dictionary:也是字典，由序号索引word
"""

# 将字典保存到当前路径,供后续使用
f_json=open('dictionary.json', 'w')
f_json.write(json.dumps(dictionary))
f_json.close()
f_json=open('reversed_dictionary.json', 'w')
f_json.write(json.dumps(reversed_dictionary))
f_json.close()


data_index = 0


# Step 3: Function to generate a training batch for the skip-gram model.
def generate_batch(batch_size, num_skips, skip_window):
    global data_index
    assert batch_size % num_skips == 0
    assert num_skips <= 2 * skip_window
    batch = np.ndarray(shape=(batch_size), dtype=np.int32)
    labels = np.ndarray(shape=(batch_size, 1), dtype=np.int32)
    span = 2 * skip_window + 1  # [ skip_window target skip_window ]
    buffer = collections.deque(maxlen=span)

    if data_index + span > len(data):   #防止数据超越边界
        data_index = 0

    buffer.extend(data[data_index:data_index + span])
    data_index += span

    for i in range(batch_size // num_skips):
        context_words = [w for w in range(span) if w != skip_window]
        words_to_use = random.sample(context_words, num_skips)#从词上下文中选取num_skips个参数用于训练

        for j, context_word in enumerate(words_to_use):
            batch[i * num_skips + j] = buffer[skip_window]      #输入的词
            labels[i * num_skips + j, 0] = buffer[context_word]     #需输出的词

        if data_index == len(data):
            #buffer[:] = data[0:span] # 这么写会报错:sequence index must be integer, not 'slice'
            for w in data[:span]:
                buffer.append(w)
            data_index = span
        else:
            buffer.append(data[data_index])
            data_index += 1

    # Backtrack a little bit to avoid skipping words in the end of a batch
    data_index = (data_index + len(data) - span) % len(data)

    return batch, labels


batch, labels = generate_batch(batch_size=8, num_skips=2, skip_window=1)

for i in range(8):
    print(batch[i], reversed_dictionary[batch[i]], '->', labels[i, 0], reversed_dictionary[labels[i, 0]])

# Step 4: Build and train a skip-gram model.

batch_size = 128
embedding_size = 128  # Dimension of the embedding vector.
skip_window = 1  # How many words to consider left and right.
num_skips = 2  # How many times to reuse an input to generate a label.
num_sampled = 64  # Number of negative examples to sample.

# We pick a random validation set to sample nearest neighbors. Here we limit the
# validation samples to the words that have a low numeric ID, which by
# construction are also the most frequent. These 3 variables are used only for
# displaying model accuracy, they don't affect calculation.
valid_size = 16  # Random set of words to evaluate similarity on.
valid_window = 100  # Only pick dev samples in the head of the distribution.
valid_examples = np.random.choice(valid_window, valid_size, replace=False)

graph = tf.Graph()

with graph.as_default():
    # Input data.
    train_inputs = tf.placeholder(tf.int32, shape=[batch_size])
    train_labels = tf.placeholder(tf.int32, shape=[batch_size, 1])
    valid_dataset = tf.constant(valid_examples, dtype=tf.int32)

    # Ops and variables pinned to the CPU because of missing GPU implementation
    with tf.device('/gpu:0'):
        # Look up embeddings for inputs.
        embeddings = tf.Variable(
            tf.random_uniform([vocabulary_size, embedding_size], -1.0, 1.0))
        embed = tf.nn.embedding_lookup(embeddings, train_inputs)

        # Construct the variables for the NCE loss
        nce_weights = tf.Variable(
            tf.truncated_normal([vocabulary_size, embedding_size],
                                stddev=1.0 / math.sqrt(embedding_size)))
        nce_biases = tf.Variable(tf.zeros([vocabulary_size]))

    # Compute the average NCE loss for the batch.
    # tf.nce_loss automatically draws a new sample of the negative labels each
    # time we evaluate the loss.
    # Explanation of the meaning of NCE loss:
    #   http://mccormickml.com/2016/04/19/word2vec-tutorial-the-skip-gram-model/
    loss = tf.reduce_mean(tf.nn.nce_loss(weights=nce_weights,
                                         biases=nce_biases,
                                         labels=train_labels,
                                         inputs=embed,
                                         num_sampled=num_sampled,
                                         num_classes=vocabulary_size))

    # Construct the SGD optimizer using a learning rate of 1.0.
    optimizer = tf.train.GradientDescentOptimizer(1.0).minimize(loss)

    # Compute the cosine similarity between minibatch examples and all embeddings.
    norm = tf.sqrt(tf.reduce_sum(tf.square(embeddings), 1, keep_dims=True))
    normalized_embeddings = embeddings / norm
    valid_embeddings = tf.nn.embedding_lookup(normalized_embeddings, valid_dataset)
    similarity = tf.matmul(valid_embeddings, normalized_embeddings, transpose_b=True)

    # Add variable initializer.
    init = tf.global_variables_initializer()

# Step 5: Begin training.
num_steps = 400001

with tf.Session(graph=graph) as session:
    # We must initialize all variables before we use them.
    init.run()
    print('Initialized')

    average_loss = 0
    for step in xrange(num_steps):
        batch_inputs, batch_labels = generate_batch(batch_size, num_skips, skip_window)
        feed_dict = {train_inputs: batch_inputs, train_labels: batch_labels}

        # We perform one update step by evaluating the optimizer op (including it
        # in the list of returned values for session.run()
        _, loss_val = session.run([optimizer, loss], feed_dict=feed_dict)
        average_loss += loss_val

        if step % 2000 == 0:
            if step > 0:
                average_loss /= 2000
            # The average loss is an estimate of the loss over the last 2000 batches.
            print('Average loss at step ', step, ': ', average_loss)
            average_loss = 0

        # Note that this is expensive (~20% slowdown if computed every 500 steps)
        if step % 10000 == 0:
            sim = similarity.eval()

            for i in xrange(valid_size):
                valid_word = reversed_dictionary[valid_examples[i]]
                top_k = 8  # number of nearest neighbors
                nearest = (-sim[i, :]).argsort()[1:top_k + 1]
                log_str = 'Nearest to %s:' % valid_word

                for k in xrange(top_k):
                    close_word = reversed_dictionary[nearest[k]]
                    log_str = '%s %s,' % (log_str, close_word)

                print(log_str)

    final_embeddings = normalized_embeddings.eval()


# 保存最终的embeding
np.save('embedding.npy', final_embeddings)

# Step 6: Visualize the embeddings.
# pylint: disable=missing-docstring
# Function to draw visualization of distance between embeddings.
def plot_with_labels(low_dim_embs, labels, filename, fonts=None):
    assert low_dim_embs.shape[0] >= len(labels), 'More labels than embeddings'
    plt.figure(figsize=(18, 18))  # in inches

    for i, label in enumerate(labels):
        x, y = low_dim_embs[i, :]
        plt.scatter(x, y)
        plt.annotate(label,
                     fontproperties=fonts,
                     xy=(x, y),
                     xytext=(5, 2),
                     textcoords='offset points',
                     ha='right',
                     va='bottom')

    plt.savefig(filename)


try:

    from sklearn.manifold import TSNE
    import matplotlib.pyplot as plt
    from matplotlib.font_manager import FontProperties

    zhfont = FontProperties(fname="/usr/share/fonts/truetype/arphic/ukai.ttc")  # 中文字体

    tsne = TSNE(perplexity=30, n_components=2, init='pca', n_iter=5000, method='exact')
    plot_only = 500
    low_dim_embs = tsne.fit_transform(final_embeddings[:plot_only, :])
    labels = [reversed_dictionary[i] for i in xrange(plot_only)]
    plot_with_labels(low_dim_embs, labels, 'tsne_w2v.png', fonts=zhfont)

except ImportError as ex:
    print('Please install sklearn, matplotlib, and scipy to show embeddings.')
    print(ex)
